The Neural Correlates of Similarity- E - Ricerca sull'intelligenza artificiale specializzata al MIT

The Neural Correlates of Similarity- E
Rule-based Generalization

Fraser Milton1, Pippa Bealing1, Kathryn L. Carpenter1,
Abdelmalek Bennattayallah1, and Andy J. Wills2

Astratto

■ The idea that there are multiple learning systems has be-
come increasingly influential in recent years, with many studies
providing evidence that there is both a quick, similarity-based
or feature-based system and a more effortful rule-based system.
A smaller number of imaging studies have also examined
whether neurally dissociable learning systems are detectable.
We further investigate this by employing for the first time in
an imaging study a combined positive and negative patterning
procedure originally developed by Shanks and Darby [Shanks,
D. R., & Darby, R. J. Feature- and rule-based generalization in
human associative learning. Journal of Experimental Psychology:
Animal Behavior Processes, 24, 405–415, 1998]. Unlike previous
related studies employing other procedures, rule generalization

in the Shanks–Darby task is beyond any simple non-rule-based
(per esempio., associative) account. We found that rule- and similarity-
based generalization evoked common activation in diverse re-
gions including the pFC and the bilateral parietal and occipital
lobes indicating that both strategies likely share a range of com-
mon processes. No differences between strategies were iden-
tified in whole-brain comparisons, but exploratory analyses
indicated that rule-based generalization led to greater activa-
tion in the right middle frontal cortex than similarity-based
generalization. Conversely, the similarity group activated the
anterior medial frontal lobe and right inferior parietal lobes
more than the rule group did. The implications of these results
are discussed. ■

INTRODUCTION

The ability to generalize information we have previously
learned to novel stimuli is fundamental for successful
functioning in our everyday environment. An enduring
and contentious question is whether this is achieved by
separable learning systems (per esempio., Ashby, Alfonse-Reese,
Turken, & Waldron, 1998; Brooks, 1978) or just a single
system (per esempio., Newell, Dunn, & Kalish, 2011; Nosofsky &
Kruschke, 2002). Multiple-system accounts typically posit the
existence of a nondeliberative ( Wills, Milton, Longmore,
Hester, & Robinson, 2013) or nonanalytic (Brooks, 1978)
processi, that is automatic (Smith, Patalano, & Jonides, 1998),
similarity-based (Milton, Longmore, & Wills, 2008), E
driven by associative (McLaren, Verde, & Mackintosh,
1994) or implicit (Ashby et al., 1998) processes. A second
system is assumed to be deliberative (Wills et al., 2013) O
analytic (Brooks, 1978), controlled (Smith et al., 1998), rule-
based (Ashby et al., 1998), and requiring of extensive cogni-
tive resources (Wills, Inkster, & Milton, 2015). In questo articolo,
we refer to these two systems as similarity- and rule-based.
Much of the evidence relevant to this debate has come
from behavioral or comparative studies. Some of this evi-
dence is consistent with multiple learning systems accounts
(per esempio., Maes et al., 2015; Ashby & Maddox, 2011; Allen &

1University of Exeter, 2University of Plymouth

Brooks, 1991; Rips, 1989; Kemler Nelson, 1984), whereas
others maintain that this evidence can be more parsimoni-
ously explained by a single system (per esempio., Edmunds, Milton,
& Wills, 2015; Wills et al., 2015; Newell, Moore, Wills, &
Milton, 2013; Stanton & Nosofsky, 2013). Consequently,
there is currently no clear consensus on this issue. A com-
plimentary and currently relatively underexplored ap-
proach is to use brain imaging to examine whether there
are neurally dissociable learning systems. One such fMRI
study, loosely based on earlier behavioral work by Allen
and Brooks (1991), was conducted by Koenig et al.
(2005), who asked participants to classify a set of cartoon
animals differing on four stimulus dimensions (per esempio., legs,
neck type). Participants in the rule condition were in-
formed of a complex rule (category membership requires
the instance to possess three of four characteristic fea-
tures for that category). In the similarity condition, par-
ticipants were not told the rule but instead asked to
make a quick decision using their first impressions about
which category a particular instance was more similar to.
Both groups were provided with trial-by-trial feedback.
Koenig et al. found that similarity-based, compared with
rule-based, categorization recruited greater activation in
bilateral temporoparietal regions as well as bilateral ante-
rior prefrontal regions (BA 10). Conversely, the rule-based
condition led to greater activation than the similarity
condition in the left frontal lobes, left inferior parietal

Journal of Cognitive Neuroscience 29:1, pag. 150–166
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lobes, and the right superior parietal lobes. One feature of
this study, Tuttavia, is that it is not clear exactly what
strategy participants in the similarity condition are employ-
ing, which makes interpretation of the imaging results
more complicated. Specifically, Koenig et al. assume that
participants are using a similarity-based approach that
presumably requires the use of most, if not all, of the di-
mensions. Although this is plausible, an alternative ex-
planation is that participants in the similarity condition
are using a simpler rule-based approach, such as a single
dimension-plus-exception strategy (per esempio., Ward & Scott,
1987), which could also result in the level of performance
obtained. In this latter case, participants in the similarity
condition are using fewer of the dimensions than those
in the rule condition.

In a study somewhat more closely based on the work
of Allen and Brooks (1991), Patalano, Smith, Jonides, E
Koeppe (2001) also observed activation in bilateral frontal
cortex in the rule-based condition that was not present in
the similarity condition. Occipital lobe and cerebellum
activation was prevalent in both conditions. Tuttavia,
significant neural differences between the groups were
relatively restricted, even though one-tailed tests were
used. Per esempio, the greater frontal lobe activation in
the rule than the similarity condition was only marginally
significant ( p = .06).

Using a slightly different approach—the criterial attri-
bute procedure, based on earlier behavioral work by
Kemler Nelson (1984)—Tracy et al. (2003) investigated
the neural correlates of family resemblance categoriza-
zione (assumed to use the similarity system) and unidimen-
sional categorization (assumed to employ the rule system).
Similar to the category structure employed by Koenig et al.
(2005), a family resemblance category (per esempio., Rosch &
Mervis, 1975) possessed a number of characteristic but
not defining features—an item did not have to possess
any single feature or features as long as it possessed
enough characteristic features (three of four typical fea-
tures) of that category. In contrasto, a unidimensional
category was based around a single defining feature that
the authors assumed required use of the rule-based
system. Tracy et al. (2003) found greater activation in the
extrastriate cortex (BA 18 and BA 19) and the left cere-
bellum for family resemblance (similarity-based) cate-
gorization than unidimensional categorization, whereas
unidimensional categorization led to greater activation in
bilateral frontal lobes than family resemblance categoriza-
zione. Tuttavia, recent behavioral model-based analysis
suggests that family resemblance categorization in the
criterial attribute procedure is often due to the use of a
single noncriterial dimension, which is a strategy not
detectable by the standard analysis employed by Tracy
et al. (for a detailed discussion, see Wills et al., 2015). Questo
again makes interpretation of the neural differences ob-
served more difficult.

In contrast to Tracy et al.’s (2003) conclusions, Milton,
Wills, and Hodgson (2009) proposed that both family

resemblance and unidimensional categorization are the
result of a single rule-based system, with family resem-
blance categorization requiring a more complex, multi-
dimensional rule than unidimensional categorization
(see also Wills et al., 2013). Consistent with their proposal,
Milton et al. found extensive common activation between
family resemblance and unidimensional categorization
including the dorsolateral frontal cortex and the anterior
cingulate. The most notable difference between groups
was the greater right ventrolateral frontal cortex activation
for family resemblance than unidimensional categoriza-
zione, which the authors proposed indicated the greater
working memory resources required to employ a multi-
dimensional rule.

A different approach was taken by Nomura et al.
(2007), who conducted an fMRI study based on the influ-
ential COVIS framework (Ashby et al., 1998); participants
viewed a series of Gabor patches and learned either a
rule-based task that possessed an easily verbalizable,
unidimensional rule (“thinner lines belong in category
UN, thicker lines in category B”), which is assumed to
encourage use of the explicit system or an information-
integration task, which requires participants to combine
information from two unrelated stimulus dimensions.
The optimal information-integration category structure
is assumed to be difficult or impossible to verbalize, Quale
should encourage use of COVIS’s implicit system. In line
with COVIS’s predictions, dissociable neural activation
was found with the medial temporal lobes more activated
in rule-based compared with information-integration
apprendimento, and the caudate body more engaged in infor-
mation-integration than rule-based learning.

Although intriguing, the category separation (cioè., IL
mean distance between category items as plotted in stim-
ulus space divided by the within-category variance along
the direction of the comparison) was smaller in the rule-
based than the information-integration condition, E
the selective attention demands were greater in the
rule-based than the information-integration condition
(as only one of the two dimensions was relevant to learn
the rule-based structure whereas both were required for
the information-integration structure) meaning that non-
essential differences could have been driving the neural
dissociations. In a recent study conducted by Carpenter,
Wills, Benattayallah, and Milton (in press) when these
nonessential differences between the rule-based and
information-integration conditions were better equated
(by comparing a conjunctive rule-based structure against
a standard information-integration structure), the pattern
of results observed by Nomura et al. (2007) did not
emerge, and instead there was extensive common over-
lap between the conditions. Inoltre, the informa-
tion-integration condition evoked greater activation in
the medial-temporal lobes than the rule-based condition,
which may reflect the greater memory demands in the
information-integration condition where no rule was
readily available. In another related study, albeit one

Milton et al.

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which used very different stimuli (the stimuli varied on
rectangle height and width of an ellipse), Milton and
Pothos (2011) compared activation between a rule-based
task and an information-integration-like task but found
minimal neural dissociations and instead found extensive
overlap of activation suggesting that both groups were
using similar neural processes.

Finalmente, Grossman et al. (2002), using a modified version
of Rips’s (1989) classic procedure, gave participants a
description of an item such as “a round object 2 inches
in diameter” who had to assign it to either the category
of “quarter” or “pizza.” The description is more similar in
size to a quarter than a pizza but a pizza has a variable
diameter (so could, in principle, be 2 inches) whereas a
quarter does not (so it cannot be 2 inches). Participants
who choose the quarter category were assumed to be
making a similarity judgment, whereas those who assign
it to the pizza category are following a rule. Grossman
et al. found that there was greater recruitment of the
left dorsolateral pFC for rule than similarity responses
whereas the right inferior parietal lobe, which they noted
is involved in overall feature configuration ( Wilkinson,
Halligan, Henson, & Dolan, 2002), was activated more for
similarity- than rule-based responses. Tuttavia, Nosofsky
and Johansen (2000) have demonstrated that the results
from this procedure can be accommodated by a simple,
single-process, exemplar-based learning system without
requiring qualitatively distinct systems for the different
strategies.

We investigate whether there are neurally separable
rule and similarity generalization systems from a different
angle using a procedure based on Shanks and Darby’s
(1998) Experiment 2, which has not previously been
examined using brain imaging. The design of this exper-
iment is shown in Figure 1. Participants took the role of
an allergist who had to determine whether the meals a
hypothetical patient, Mr. X, eats will cause an allergic
reaction or not. Letters in Figure 1 stand for particular
foods (per esempio., pasta or eggs), + indicates that an allergic
reaction will develop, and − indicates that no allergic
reaction will occur. During training, participants learn
two complete negative patterning problems (per esempio., A+,

Figura 1. The training and test trial types in the Shanks and Darby
(1998, Experiment 2) allergy prediction task; letters indicate foods
eaten by a hypothetical patient Mr. X, + = patient develops an allergic
reaction; − = patient does not develop an allergy reaction; ? = no
feedback given.

B+, AB−) and two complete positive patterning prob-
lems (per esempio., C−, D−, CD+). Critically, Tuttavia, there
are also four incomplete patterning problems—for exam-
ple, participants are trained on I+ and J+ but not on the
outcome of I and J combined and trained that eating KL
together leads to an allergic reaction but not what hap-
pens when K and L are eaten separately. During the test
phase, as well as being tested on items they studied dur-
ing training (per esempio., I+, J+, and KL+), participants have to
generalize the knowledge they have obtained to what will
henceforth be referred to as the critical items (per esempio., IJ, K,
and L; shown in bold in Figure 1) and are provided no
feedback on their responses.

In the case of IJ, if participants are using a similarity-
based strategy then they should predict an allergic re-
action as it is similar to I and J, both of which lead to
an allergic reaction alone. Equally, when presented with
K or L alone they should predict an allergic reaction be-
cause they are similar to KL which results in an allergic
reaction. In contrasto, if participants have learned the
“opposites” rule from training—single foods predict the
opposite to their compounds—they can use this to gen-
eralize to novel items. In questo caso, IJ should lead to no
allergic reaction, because it is the opposite outcome to I
or J when presented alone. Allo stesso modo, K or L, when pre-
sented separately, should lead to no allergic reaction as
this is the opposite to KL, which resulted in an allergic
reaction. Shanks and Darby (1998) found that partici-
pants with high accuracy during training produced more
rule-based responses for these critical test items than
participants with lower accuracy. They explained this by
postulating that there is a transition from a similarity to a
rule-based approach, which can only be used when the
basic associations have been acquired (see also Wills,
Graham, Koh, Mclaren, & Rolland, 2011).

While using a novel procedure to compare the neural
correlates of the purported rule and similarity systems is
of value in itself, the Shanks–Darby procedure has some
particular advantages that make it well equipped to pro-
vide new insight into this debate. Primo, both similarity-
and rule-based responses require utilizing the same
number of stimulus dimensions. This is in contrast to many
of the studies described above (per esempio., Milton & Pothos,
2011; Milton et al., 2009; Nomura et al., 2007; Tracy et al.,
2003) where the number of stimulus dimensions utilized
in the similarity condition seem unlikely to be the same
as in the rule conditions (it may either be more, as is
commonly assumed, or sometimes less, depending on
how participants approach the task in the similarity con-
dizione). Across a range of different procedures, categoriz-
ing by a larger number of dimensions is more effortful
than categorizing by fewer dimensions (per esempio., Edmunds
et al., 2015; Wills et al., 2015; Milton & Wills, 2004). È
plausible that this could be driving the difference in neural
activation between the groups, rather than indicating the
involvement of qualitatively different systems. A further
advantage of employing the Shanks–Darby procedure is

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that a full explanation of the “opposites” rule generalization
is commonly thought to be beyond published associative
accounts (see Maes et al., 2015, for a further discussion)
allowing clear inferences to be drawn. This does not appear
to be the case for the other procedures described above.
For instance, non-rule-based accounts, such as Kruschke’s
(1992) ALCOVE model, which have a mechanism for di-
mensional attention, are able to account for the purported
rule-based classification in these tasks.

In summary, Poi, this study uses a novel approach to
investigate the neural differences between rule-based
and similarity-based generalization. We predicted, based
on previous behavioral and comparative work with this
procedure (Maes et al., 2015; Wills et al., 2011), that we
would observe neural differences between the generali-
zation strategies. In particular, we hypothesized that
there would be greater frontal lobe activation in the
rule-based condition than the similarity-based condition
(per esempio., Milton et al., 2009; Nomura & Reber, 2008; Grossman
et al., 2002; Patalano et al., 2001). Our prediction for which
regions would be implicated in similarity-based generali-
zation was more tentative given the greater heterogeneity
in previous studies but viable options a priori included the
right inferior parietal lobes (Grossman et al., 2002) E
the occipital lobes (per esempio., Nomura et al., 2007; Patalano
et al., 2001)/extrastriate cortex (Tracy et al., 2003).

METHODS

Participants

Sixty-two right-handed participants were recruited from
the University of Exeter participant pool. Participants
were either volunteers, received course credits, or were
paid £7. Participants all gave informed consent accord-
ing to procedures approved by the Psychology Ethics
Committee, University of Exeter. A learning criterion was
set as significantly above chance accuracy in the second
half of training to ensure that all participants included
in the analyses had clear evidence of learning. Without
Questo, one could not reasonably expect true generalization
to occur. This resulted in the exclusion of 10 participants.
A further 14 participants did not show clear evidence of
either rule- or similarity-based generalization—defined as
significantly above chance (64.6%) strategy-consistent re-
sponding for the critical test trials. These participants
who did not adopt a clear strategy would likely obscure
any differences that emerged between participants who
did demonstrate clear rule or similarity generalization
so we consequently excluded them from all the principal
analyses. Noi, Tuttavia, consider the test phase data for
these 14 participants who used a mixture of rule and sim-
ilarity consistent responses separately. This left 38 partici-
pants in total for our principal analyses; 24 rule-based
responders and 14 similarity-based responders. Questo
trend for a greater proportion of rule responders was not
significant, χ2(1) = 2.632, p = .105.

Stimuli

The stimuli (food names) were identical to those used in
Experiment 2 of Shanks and Darby (1998). For half of the
participants, the food names A–P (Guarda la figura 1) were
cheese, garlic, milk, mushrooms, seafood, red meat, olive
oil, coffee, banana, eggs, orange squash, bread, avocado,
peanuts, pasta, and chocolate. For the other half, IL
foods assigned to A/B were exchanged for those assigned
to C/D and likewise for E/F and G/H, I/J and K/L, E
M/N and O/P.

Procedure

Before entering the scanner, participants were asked to
take the role of a food allergist and to learn when Mr. X
would develop an allergic reaction after eating a meal
containing certain foods. Twenty-nine participants were
additionally provided with instructions outlining the rule
(per esempio., “If Mr. X is allergic to a food when it is presented on
its own, he won’t be allergic to it when it is presented
together with another food. Conversely, if Mr. X is not al-
lergic to a food when it is presented on its own then he
will be if it is presented in combination with another
food”), E 33 participants were provided with instruc-
tions designed to encourage a similarity-based approach
(“When making a response, please use your intuition as
to what you feel is the correct answer based on what
you have previously seen”). The rationale behind this
was to facilitate obtaining a sufficient number of partici-
pants who consistently categorized the critical trials by
either a similarity or a rule-based approach rather than
to look at the neural effects of differing instructions per
se. In practice, Tuttavia, this instructional manipulation
had no significant impact on the strategy used (we sus-
pect, in hindsight, that it would have been more effective
if, like the training items, it had been presented inside the
scanner in the same context as where learning took place)
and will, consequently, not be discussed further.

Visual stimuli were presented on a back-projection
screen positioned at the foot end of the MRI scanner
and viewed via a mirror mounted on a head coil. Button-
press responses and RTs were measured using a fiber-
optic button box. E-Prime (Psychological Software Tools,
2002, www.pstnet.com) was used for the presentation
and timing of stimuli and collection of response data.

In the training phase, participants received six blocks
of trials, divided into two scanning runs of three blocks,
with each of the 18 training stimuli (Guarda la figura 1) pre-
sented twice in each block in a random order. Each trial
began with a white screen lasting a random interval be-
tween 500 E 4000 msec, before a black fixation cross
was presented in the middle of the screen for 250 msec.
A meal, food names presented in black font, was then
presented in the middle of a white screen for 3000 msec
during which time participants indicated whether it
would lead to an allergic reaction (by pressing the left

Milton et al.

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button box key) or would not lead to an allergic reaction
(by pressing the right button box key). Following this,
feedback (“Correct,” in blue or “Incorrect,” in red) era
presented for 500 msec. For participants who failed to
respond when the meal was presented, the message
“Timeout!!" (in red) appeared instead. The next trial then
immediately began. At the end of each block of 36 trials,
the average accuracy on that block was displayed on the
screen for 12 sec, before the next block began.

The test phase included all 24 stimuli, comprising the
18 training stimuli plus the six critical generalization stim-
uli (shown in bold in Figure 1). IL 18 training stimuli
were presented once in each block, whereas the six crit-
ical trials were each presented twice leading to 30 trials in
each of the four blocks. Stimuli were presented in a ran-
dom order. The intratrial structure was identical to the
training phase, except that after a response a 500-msec
blank screen appeared rather than feedback. If no re-
sponse was made, a time out message appeared as in
the training phase.

fMRI Data Acquisition

Images were collected using a 1.5-T Gyroscan magnet
equipped with a Sense coil (Philips, Amsterdam, IL
Netherlands). A T2*-weighted echo-planar sequence was
used (repetition time = 3000 msec, echo time = 45 msec,
flip angle = 90°, 32 transverse slices, field of view =
240 mm, 3.5 × 2.5 × 2.5 mm). The training phase com-
prised two runs of 240 scans, and the test phase one run
Di 260 scans. Five dummy scans were performed before
the start of each stimulus sequence. Standard volumetric
anatomical MRI was performed after functional scanning
by using a 3-D T1-weighted pulse sequence (repetition
time = 25 msec, echo time = 4.1 msec, flip angle = 30°,
160 axial slices, 1.6 × 0.9 × 0.9 mm).

Analysis of fMRI Data

Analyses were carried out using SPM8 software (www.fil.
ion.ucl.ac.uk/spm). Functional images were corrected for
acquisition order, realigned to the mean image, E
resliced to correct for motion artifacts. The realigned
images were coregistered with the structural T1 volume,
and the structural volumes were spatially normalized.
The spatial transformation was applied to the realigned
T2* volumes, which were spatially smoothed using a
Gaussian kernel of 8 mm FWHM. Data were high-pass
filtered (1/128 Hz) to account for low-frequency drifts.
The BOLD response was modeled by a canonical hemo-
dynamic response function.

All analyses were conducted using the general linear
modello. In the individual participant models, the critical
trials that were consistent with their overall favored
strategy (cioè., rule- or similarity-based generalization)
were included as one regressor, whereas critical trials
inconsistent with this approach were a second regressor.

The familiar items were partitioned into correct and in-
correct responses. The duration of each event was mod-
eled as the participant’s RT for that trial (see Grinband,
Wager, Lindquist, Ferrera, & Hirsch, 2008, for the advan-
tages of using this “variable epoch” approach). Time outs
were included as a fifth regressor of no interest. The six
head movement parameters were included as additional
covariates. Contrasts comparing strategy-consistent re-
sponses for the critical trials were subtracted against
the implicit baseline (the intervals between the five event
types listed above; cf. Milton et al., 2009; Tracy et al.,
2003, for a similar approach), and correct familiar trials
were likewise compared with the implicit baseline. These
comparisons were then included in random-effects anal-
yses. For these analyses, participants were divided into
those who provided clear evidence of either similarity
or rule-based generalization (cioè., significantly above
chance strategy-consistent responding on the critical
generalization trials).

Whole-brain analyses were completed using a com-
bined statistical threshold of p < .001 (uncorrected) and a threshold of 100 contiguous voxels, which together produce an overall corrected threshold of p < .05. These values were estimated using 3dClustSim as implemented in the AFNI toolbox (afni.nimh.nih.gov/afni/). For this, we used a smoothness estimate of 10.1 × 10.1 × 9.6 mm (this was a group level estimate calculated in SPM8 using the group residuals from the general linear model, e.g., Kiebel, Poline, Friston, Homes, & Worsley, 1999). In addition, to measure common activation between rule- based and similarity-based participants, conjunction anal- yses were performed. To do this, the relevant contrasts were combined using a logical “and” function through the minimum statistic to the conjunction null hypothesis (MS/CN; Nichols, Brett, Andersson, Wager, & Poline, 2005) technique implemented in SPM8. Both contrasts were again conducted with a combined threshold of p < .001 (uncorrected) and a cluster threshold of 100 contiguous voxels. Note that this analysis is conservative because it reveals only those regions significantly acti- vated for both the rule ( p < .05, corrected) and the sim- ilarity ( p < .05, corrected) conditions. After performing the whole-brain analyses, we decided to conduct more exploratory ROI analyses (using the WFU Pickatlas, e.g., Maldjian, Laurienti, Burdette, & Kraft, 2003) when directly comparing rule and similarity gen- eralization. These post hoc ROI analyses were based on our a priori predictions of regions we thought would be differentially involved between strategies and comprised the pFC (e.g., Milton & Pothos, 2011; Milton et al., 2009), the occipital lobes/extrastriate cortex (BA 18 and BA 19; e.g., Nomura et al., 2007; Tracy et al., 2003), and the right inferior parietal lobes (Grossman et al., 2002). Although these exploratory analyses should accordingly be taken with some caution, we believe that they help to charac- terize better the nature of our results, which is particu- larly important given that this is the first imaging study 154 Journal of Cognitive Neuroscience Volume 29, Number 1 l l / / j t t f / i t . : / / D o w n l o a D d o e w d n l f o r a o d m e d h t f t r p o : m / D / o h m w t i n t t p o p : a r / d c / e . d m s i f i r t o l p m v r e c h r . c p s h i a l d i v i r r e e . r c c t c . o m h m a / i j e r d o . u c c n o o / m c a n / r j a t o r i t c i c c n l e / e - a p - r d p t d i 2 f 9 c / l 1 2 e 9 1 - / 5 p 1 0 d / f 1 1 9 / 5 5 2 0 2 9 / 1 / 1 4 1 9 7 / 1 1 o 4 5 c 2 0 n 5 / _ 2 a 1 / _ 7 j 0 8 o 1 5 c 0 7 n 2 5 4 _ 7 a p / _ d j 0 o 1 b c 0 y n 2 g _ 4 u a . e _ p s 0 t d 1 o f 0 n 2 b 0 4 y 8 . p S M d e I f p T e b m L y i b b e g r r u a 2 e r 0 s i 2 t 3 e s o n u s 0 e 3 r M o a n y / j / f / t / . 1 2 7 0 2 M 1 a y 2 0 2 1 mance for the familiar items (i.e., those seen during the training phase) across blocks is displayed in Figure 2A. The average accuracy (collapsed across blocks) for both the rule (M = .923, SD = .083, t(23) = 25.111, p < .001) and similarity (M = .763, SD = .129; t(13) = 7.616, p < .001) groups was significantly above chance, although as in the training phase, rule-based participants had higher accuracy than similarity-based participants, t(19.3) = 4.151, p < .001. Median RTs were nonsignificantly longer in the similarity group (1208 msec) than the rule group (1060 msec), t(18.4) = 1.78, p = .09. Of particular importance, given our interest in general- ization strategies, both the rule and similarity groups used their preferred strategy significantly above chance levels (rule group, M = .862, SD = .106, t(24) = 16.691, p < .001; similarity group, M = .825, SD = .095, t(13) = 12.868, p < .001) for the critical test items (see Fig- ure 2B), and there was no significant difference in strategy- consistent responding between groups, t(29.3) = 1.074, p = .292, with substantial evidence for the null, BF (Bayes factor) = 0.25.2 Median RTs were nonsignificantly shorter in the similarity group (1119 msec) than the rule group (1294 msec), t(22.3) = 1.86, p = .08. Although participants were classified on the basis of being either rule-consistent or similarity-consistent col- lapsed across all critical items, it does not necessarily follow that both the critical compound and element stimuli show this pattern. We therefore consider generalization to com- pound and element stimuli separately as in past work (e.g., Wills et al., 2011; Shanks & Darby, 1998). The mean prob- ability of predicting an allergic reaction to the critical com- pound stimuli (i.e., IJ and MN) is shown in Figure 3A. As expected, there was a significant interaction between strat- egy used and stimulus type, F(1, 36) = 382.02, p < .001. No other main effects or interactions were significant ( ps >
.25). The rule group showed rule-consistent generalization
to compounds, T(23) = 25.87, P < .001, whereas the Figure 2. (A) Accuracy of rule-based and similarity-based responders for familiar items during the test phase. (B) Proportion of critical trials across blocks which are consistent with the strategy participants were assigned to. of the Shanks–Darby procedure. For these analyses, we used thresholds of p < .001 and 64 contiguous voxels, which together produce an overall corrected threshold of p < .05, as estimated by 3dClustSim. Normalized MNI space coordinates were transformed to Talairach space (imaging.mrccbu.cam.ac.uk/imaging/MniTalairach) to establish activation sites as per the atlas of Talairach and Tournoux (1988).1 RESULTS Behavioral Analyses Training Phase The proportions of timeouts were low in both the rule (M = .015, SD = .021) and similarity (M = .020, SD = .018) groups, and there was no significant difference be- tween them, t(31.1) = .829, p = .413. One-sample t tests revealed that the average performance in the second half of training (Blocks 4–6) was significantly above chance for both the rule-based (M = .872, SD = .092; t(23) = 19.647, p < .001) and the similarity-based (M = .739, SD = .088; t(13) = 10.217, p < .001) groups, although, as in Shanks and Darby (1998), the rule group had higher accuracy than the similarity group, t(28.6) = 4.409, p < .001. Median RTs were longer in the similarity group (1291 msec) than in the rule group (1010 msec), t(21.8) = 3.874, p < .001. Test Phase The proportions of timeouts were again low (rule group: M = .012, SD = .016; similarity group: M = .026, SD = .031), and there was no significant difference between conditions, t(17.2) = 1.608, p = .126. Average perfor- Figure 3. (A) Mean probability of predicting an allergic reaction for the critical compound stimuli. (B) Mean probability of predicting an allergic reaction for the critical element stimuli. Also shown are difference-adjusted 95% confidence intervals for the between-subject effects (Baguley, 2012). Milton et al. 155 l l / / j f / t t i t . : / / D o w n l o a D d o e w d n l f o r a o d m e d h t f t r p o : m / D / o h m w t i n t t p o p : a r / d c / e . d m s i f i r t o l p m v r e c h r . c p s h i a l d i v i r r e e . r c c t c . o m h m a / i j e r d o . u c c n o o / m c a n / r j a t o r i t c i c c n l e / e - a p - r d p t d i 2 f 9 c / l 1 2 e 9 1 - / 5 p 1 0 d / f 1 1 9 / 5 5 2 0 2 9 / 1 / 1 4 1 9 7 / 1 1 o 4 5 c 2 0 n 5 / _ 2 a 1 / _ 7 j 0 8 o 1 5 c 0 7 n 2 5 4 _ 7 a p / _ d j 0 o 1 b c 0 y n 2 g _ 4 u a . e _ p s 0 t d 1 o f 0 n 2 b 0 4 y 8 . p S M d e I f p T e b m L y i b b e g r r u a 2 e r 0 s i 2 t 3 e s o n u s 0 e 3 r M o a n y / j . f / / t / 1 2 7 0 2 M 1 a y 2 0 2 1 similarity group showed similarity-consistent generali- zation, t(13) = 8.21, p < .001. Median RTs were non- significantly shorter in the similarity group (1313 msec) than the rule group (1457 msec), F(1, 36) = 1.29, p = .26. No other effects were significant ( ps > .12).

Figure 3B shows the probability of predicting an aller-
gic reaction to the critical element stimuli (cioè., K/L and
O/P). There was again a significant interaction between
strategy used and stimulus type, F(1, 36) = 201.80, P < .001. No other main effects or interactions were signifi- cant ( ps > .4). The rule group showed rule-consistent
generalization to elements, T(23) = 11.75, P < .001, whereas the similarity group showed similarity-consistent generalization, t(13) = 9.43, p < .001. Median RTs were nonsignificantly shorter in the similarity group (1123 msec) than the rule group (1289 msec), F(1, 36) = 3.96, p = .054. No other main effects or interactions were significant ( ps > .06).

Imaging Analyses

Training

A contrast comparing all trials and groups against the
implicit baseline revealed extensive activation including
diverse regions of the bilateral pFC, bilateral parietal
lobes, and bilateral occipital lobes (see Figure 4A). Noi
then compared performance on early training (Blocks
1–3) to late training (Blocks 3–6) for all trials and partic-
ipants. A large cluster including the caudate head and
body, which have been extensively linked to category
apprendimento (per esempio., Seger, 2008), and the thalamus, was acti-
vated more early in training than later in training (peak
voxel: x = 16, y = −32, z = 16). Conversely, the right
inferior frontal gyrus (peak voxel: x = 24, y = 25, z =
−5) and the anterior cingulate/medial prefrontal gyrus
(peak voxel: x = 8, y = 30, z = 22) were activated more
late in training than early in training.

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T

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/

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Figura 4. (UN) Brain regions activated across all trials and all participants during training. (B) Regions activated by correct responses by the rule
group during training. (C) Regions activated by correct responses in the similarity group during training. (D) Common brain regions activated by
correct responses in the similarity and rule groups. The coordinates indicate the origin for the image displayed. Lighter colors indicate higher
z scores.

156

Journal of Cognitive Neuroscience

Volume 29, Numero 1

Tavolo 1. Regions Commonly Activated by Rule-based and Similarity-based Generalization in the Training Phase

Talairach Coordinates

Region

Cluster Size

Left anterior cingulate

1176

Right anterior cingulate

Left medial frontal gyrus

Left middle frontal gyrus

5721

Left precuneus

Left inferior parietal lobe

Right inferior occipital gyrus

3147

Right occipital lobe

Right middle occipital gyrus

Left insula

Left inferior frontal gyrus

Right inferior frontal gyrus

Right superior parietal lobe

Right precuneus

Right superior parietal lobe

Right precentral gyrus

185

196

406

354

−6

−8

−50

−28

−32

−30

sì

−67

−50

−82

−74

−84

−52

−62

−58

−11

−14

−7

−4

−5

z Score

6.59

5.98

5.89

6.40

6.36

6.34

5.83

5.71

5.66

5.65

4.90

5.34

4.68

4.19

4.66

4.18

3.87

All activations significant at p < .001. Indented rows indicate voxels in the same cluster as the nonindented row above them. BA = Brodmann’s area. For the rule group, comparing correct responses against the baseline revealed extensive activation including in the pFC, parietal lobes, and the occipital lobes (Figure 4B). Similar regions were recruited by the similarity group (Figure 4C). This extensive common overlap of activa- tion was confirmed by a conjunction analysis (Figure 4D; Table 1). When directly comparing the groups, no regions were more activated by the similarity group than the rule group; however, the bilateral posterior cingulate/pre- cuneus (cluster size: 166 voxels) was engaged more in the rule group than the similarity group (Figure 5A). An exploratory analysis of this rule–similarity contrast, with Figure 5. (A) Brain regions more activated by the rule group than the similarity group across all blocks of training. (B) Brain regions more activated by rule-based responders than similarity responders across the second half of training. The coordinates indicate the origin for the image displayed. Lighter colors indicate higher z scores. l l / / j t t f / i t . : / / D o w n l o a D d o e w d n l f o r a o d m e d h t f t r p o : m / D / o h m w t i n t t p o p : a r / d c / e . d m s i f i r t o l p m v r e c h r . c p s h i a l d i v i r r e e . r c c t c . o m h m a / i j e r d o . u c c n o o / m c a n / r j a t o r i t c i c c n l e / e - a p - r d p t d i 2 f 9 c / l 1 2 e 9 1 - / 5 p 1 0 d / f 1 1 9 / 5 5 2 0 2 9 / 1 / 1 4 1 9 7 / 1 1 o 4 5 c 2 0 n 5 / _ 2 a 1 / _ 7 j 0 8 o 1 5 c 0 7 n 2 5 4 _ 7 a p / _ d j 0 o 1 b c 0 y n 2 g _ 4 u a . e _ p s 0 t d 1 o f 0 n 2 b 0 4 y 8 . p S M d e I f p T e b m L y i b b e g r r u a 2 e r 0 s i 2 t 3 e s o n u s 0 e 3 r M o a n y / j / / f t / . 1 2 7 0 2 M 1 a y 2 0 2 1 Milton et al. 157 more liberal thresholds ( p < .001, 25 contiguous voxels) and which should consequently be taken with caution, revealed two clusters in right middle frontal gyrus (first cluster, peak voxel x = 32, y = 24, z = 15, cluster size: 41 voxels; second cluster, peak voxel, x = 28, y = 23, z = 39, cluster size: 50 voxels). No differences emerged between groups when consid- ering just the first half of training. When looking at the second half of training alone, there were again no areas more activated by the similarity group than the rule- based group. However, the bilateral posterior cingulate/ precuneus was again activated more by the rule group than the similarity group, and the right anterior cingulate/medial frontal gyrus was also engaged (see Figure 5B). Test Critical trials (generalization). A number of brain re- gions were activated by similarity responders including bilateral inferior and superior parietal lobes, right middle occipital gyrus, and left medial frontal gyrus (Figure 6A). Rule-based responders engaged the left superior parietal lobes, bilateral inferior parietal lobes, bilateral middle frontal gyrus, left medial frontal gyrus, right inferior frontal gyrus, and bilateral occipital lobes (Figure 6B). A conjunc- tion analysis (see Figure 6C; Table 2) revealed extensive common overlap of activation between the similarity- and rule-based participants, which included the left supe- rior parietal lobes, bilateral inferior parietal lobes, bilateral medial frontal gyrus, left middle frontal gyrus, and the bilateral occipital gyrus. Next, we directly contrasted brain activation between the similarity and rule groups. In contrast to the exten- sive common activation, no differences were identified in whole-brain analyses. However, in the exploratory ROI analyses (comprising the pFC, right inferior parietal lobes, and bilateral occipital lobes, with thresholds of p < .001 and 64 contiguous voxels), we found that the right middle frontal gyrus (see Figure 7A; BA 9) was acti- vated more for the rule group than the similarity group (in the same region as identified by the exploratory anal- ysis documented in the rule–similarity comparison for the training phase). In contrast, we observed greater activa- tion in the anterior medial frontal lobes (BA 10) and the right inferior parietal lobes (BA 40) for the similarity group compared with the rule-based group (Figure 7B). Element versus compound critical stimuli. As a sup- plementary question, we assessed whether there were activation differences in the element (i.e., K/L and O/P) and compound (i.e., IJ and MN) critical trials. As before, only trials that were consistent with the preferred strat- egy of the participants (i.e., rule- or similarity-based) were included. For the rule group, there was greater activation in the occipital lobes/cerebellum and the left caudate body for the compound stimuli than for the l l / / j f / t t i t . : / / D o w n l o a D d o e w d n l f o r a o d m e d h t f t r p o : m / D / o h m w t i n t t p o p : a r / d c / e . d m s i f i r t o l p m v r e c h r . c p s h i a l d i v i r r e e . r c c t c . o m h m a / i j e r d o . u c c n o o / m c a n / r j a t o r i t c i c c n l e / e - a p - r d p t d i 2 f 9 c / l 1 2 e 9 1 - / 5 p 1 0 d / f 1 1 9 / 5 5 2 0 2 9 / 1 / 1 4 1 9 7 / 1 1 o 4 5 c 2 0 n 5 / _ 2 a 1 / _ 7 j 0 8 o 1 5 c 0 7 n 2 5 4 _ 7 a p / _ d j 0 o 1 b c 0 y n 2 g _ 4 u a . e _ p s 0 t d 1 o f 0 n 2 b 0 4 y 8 . p S M d e I f p T e b m L y i b b e g r r u a 2 e r 0 s i 2 t 3 e s o n u s 0 e 3 r M o a n y / j / . f / / t 1 2 7 0 2 M 1 a y 2 0 2 1 Figure 6. (A) Brain regions significantly activated by similarity responders during the critical trials. (B) Brain regions significantly activated by rule-based responders during the critical trials. (C) Common brain regions activated by similarity- and rule-based responders during the critical trials. The coordinates indicate the origin for the image displayed. Lighter colors indicate higher z scores. 158 Journal of Cognitive Neuroscience Volume 29, Number 1 Table 2. Regions Commonly Activated by Rule-based and Similarity-based Generalization in the Critical Generalization Trials Talairach Coordinates Region Cluster Size Right superior parietal lobe 566 Right precuneus Right inferior parietal lobe Left superior parietal lobe 1040 Left precuneus Left parietal lobe Left middle occipital gyrus Left middle occipital gyrus Left inferior occipital gyrus Right middle occipital gyrus Right middle occipital gyrus Right occipital lobe Left medial frontal gyrus Right medial frontal gyrus Left middle frontal gyrus Left precentral gyrus 556 425 329 104 BA 7 19 40 7 7 39 18 18 19 18 18 17 6 6 9 6 x 32 30 34 −26 −26 −28 −26 −26 −38 24 16 22 −2 8 −50 −38 y −58 −68 −50 −62 −67 −62 −91 −84 −76 −89 −94 −88 16 16 4 0 z 51 29 41 44 27 36 10 −4 −5 10 14 −2 45 42 33 33 z Score 5.40 4.77 4.36 5.09 5.05 4.97 4.82 4.74 4.23 4.44 4.21 3.97 4.40 3.94 3.63 3.46 All activations significant at p < .001. Indented rows indicate voxels in the same cluster as the nonindented row above them. BA = Brodmann’s area. element stimuli (see Figure 8A). In contrast, no regions were more activated for the element stimuli than the compound stimuli. For the similarity group, no areas were more active for the compound stimuli than the element stimuli, although the occipital lobes were, as for the rule-based group, activated at lower thresholds (this could reflect, in part, the smaller sample size of the similarity group compared with the rule group). However, the left precentral/postcentral gyrus was acti- vated more for the element stimuli than the compound stimuli (Figure 8B). When comparing the rule and similar- ity groups, no significant differences emerged. Familiar items. Although not our primary focus, we also examined the brain activation for the familiar items to supplement the critical generalization trials analyses. Participants in the similarity-based group activated a diverse set of regions including bilateral occipital gyrus, left inferior parietal lobes, and bilateral middle frontal gyrus (Figure 9A). Rule-based generalizers recruited the l l / / j t t f / i t . : / / D o w n l o a D d o e w d n l f o r a o d m e d h t f t r p o : m / D / o h m w t i n t t p o p : a r / d c / e . d m s i f i r t o l p m v r e c h r . c p s h i a l d i v i r r e e . r c c t c . o m h m a / i j e r d o . u c c n o o / m c a n / r j a t o r i t c i c c n l e / e - a p - r d p t d i 2 f 9 c / l 1 2 e 9 1 - / 5 p 1 0 d / f 1 1 9 / 5 5 2 0 2 9 / 1 / 1 4 1 9 7 / 1 1 o 4 5 c 2 0 n 5 / _ 2 a 1 / _ 7 j 0 8 o 1 5 c 0 7 n 2 5 4 _ 7 a p / _ d j 0 o 1 b c 0 y n 2 g _ 4 u a . e _ p s 0 t d 1 o f 0 n 2 b 0 4 y 8 . p S M d e I f p T e b m L y i b b e g r r u a 2 e r 0 s i 2 t 3 e s o n u s 0 e 3 r M o a n y / j / / / f t . Figure 7. (A) Brain regions more activated by the rule-based responders than similarity responders for the critical trials. (B) Brain regions more activated by similarity responders than rule-based responders for the critical trials. The coordinates indicate the origin for the image displayed. Lighter colors indicate higher z scores. 1 2 7 0 2 M 1 a y 2 0 2 1 Milton et al. 159 Figure 8. (A) Regions more activated by compound items than element items during the critical generalization trials for the rule-based group. (B) Regions more activated by element items than compound items during the critical generalization trials for the similarity group. bilateral occipital lobes, the left superior parietal lobes, and the left inferior and middle frontal gyrus (Figure 9B). A conjunction analysis indicated common activation be- tween the similarity- and rule-based responders in the bi- lateral occipital lobes, the left superior parietal lobes, and left postcentral gyrus (see Figure 9C; Table 3). This pattern is, broadly speaking, similar to what we observed in the training phase with the same stimuli. No differences emerged between the rule and similar- ity groups in either whole-brain or our exploratory ROI analyses. This is perhaps not that surprising as these anal- yses are considerably less sensitive than the analogous critical trials analyses given that they do not directly mea- sure generalization and one cannot determine at the in- dividual trial level whether a response is rule or similarity consistent. As a further exploratory analysis, we used the l l / / j f / t t i t . : / / D o w n l o a D d o e w d n l f o r a o d m e d h t f t r p o : m / D / o h m w t i n t t p o p : a r / d c / e . d m s i f i r t o l p m v r e c h r . c p s h i a l d i v i r r e e . r c c t c . o m h m a / i j e r d o . u c c n o o / m c a n / r j a t o r i t c i c c n l e / e - a p - r d p t d i 2 f 9 c / l 1 2 e 9 1 - / 5 p 1 0 d / f 1 1 9 / 5 5 2 0 2 9 / 1 / 1 4 1 9 7 / 1 1 o 4 5 c 2 0 n 5 / _ 2 a 1 / _ 7 j 0 8 o 1 5 c 0 7 n 2 5 4 _ 7 a p / _ d j 0 o 1 b c 0 y n 2 g _ 4 u a . e _ p s 0 t d 1 o f 0 n 2 b 0 4 y 8 . p S M d e I f p T e b m L y i b b e g r r u a 2 e r 0 s i 2 t 3 e s o n u s 0 e 3 r M o a n y / j t / f / . / 1 2 7 0 2 M 1 a y 2 0 2 1 Figure 9. (A) Brain regions engaged by the similarity responders for the familiar items during test. (B) Brain regions engaged by the rule-based responders for the familiar items during test. (C) Brain regions commonly engaged by the similarity- and rule-based responders for the familiar items during test. The coordinates indicate the origin for the image displayed. Lighter colors indicate higher z scores. 160 Journal of Cognitive Neuroscience Volume 29, Number 1 Table 3. Regions Commonly Activated by the Rule and Similarity Groups during Test for the Familiar Items Talairach Coordinates Region Cluster Size BA Right superior parietal lobe Right parietal lobe Left middle occipital gyrus Left middle occipital gyrus Left cuneus Left superior parietal lobe Left precuneus Left superior parietal lobe 154 565 858 Right middle occipital gyrus 564 Right middle occipital gyrus Right middle occipital gyrus Left anterior cingulate 244 Right superior frontal gyrus Left superior frontal gyrus 7 39 18 18 17 7 19 7 18 18 18 32 8 6 x 30 30 −36 −28 −14 −30 −28 −30 14 38 30 −8 2 −6 y −49 −56 −86 −80 −91 −56 −68 −62 −91 −82 −87 17 18 8 z 39 38 −2 −6 8 40 38 49 14 1 4 38 47 49 z Score 5.37 4.70 5.17 5.08 5.01 5.07 4.88 4.81 4.47 4.44 4.33 4.45 3.86 3.54 All activations significant at p < .001. Indented rows indicate voxels in the same cluster as the nonindented row above them. BA = Brodmann’s area. WFU Pickatlas (Maldjian et al., 2003) to construct a mask containing all regions identified in the similarity–rule analysis of the critical items previously reported (Fig- ure 7B) and identified whether there was any activation in these areas for the familiar items at thresholds of p < .005 (uncorrected) and 10 contiguous voxels. This analy- sis revealed activation in the right inferior parietal lobes (peak voxel x = 59, y = −37, z = 31, cluster size: 37 vox- els). We also conducted the same type of analysis for the right middle frontal gyrus region (Figure 7A) that was more activated in the rule than the similarity condition for the critical generalization trials but did not detect any activation here. Participants with a mixture of rule- and similarity- consistent responses. Fourteen participants met the learning criterion for training but showed no clear pat- tern of similarity- or rule-based responding for the critical generalization trials and were consequently excluded from the analyses above. Nevertheless, given that these par- ticipants had a mixture of both types of strategy (rule- consistent, M = .501; similarity-consistent, M = .499), they Figure 10. (A) Regions activated by similarity-consistent responses for the group who displayed a mixture of both strategies. (B) Regions activated by rule-consistent responses for the group who displayed a mixture of both strategies. l l / / j f / t t i t . : / / D o w n l o a D d o e w d n l f o r a o d m e d h t f t r p o : m / D / o h m w t i n t t p o p : a r / d c / e . d m s i f i r t o l p m v r e c h r . c p s h i a l d i v i r r e e . r c c t c . o m h m a / i j e r d o . u c c n o o / m c a n / r j a t o r i t c i c c n l e / e - a p - r d p t d i 2 f 9 c / l 1 2 e 9 1 - / 5 p 1 0 d / f 1 1 9 / 5 5 2 0 2 9 / 1 / 1 4 1 9 7 / 1 1 o 4 5 c 2 0 n 5 / _ 2 a 1 / _ 7 j 0 8 o 1 5 c 0 7 n 2 5 4 _ 7 a p / _ d j 0 o 1 b c 0 y n 2 g _ 4 u a . e _ p s 0 t d 1 o f 0 n 2 b 0 4 y 8 . p S M d e I f p T e b m L y i b b e g r r u a 2 e r 0 s i 2 t 3 e s o n u s 0 e 3 r M o a n y / j / . f / / t 1 2 7 0 2 M 1 a y 2 0 2 1 Milton et al. 161 provide an opportunity to look at the neural correlates of rule- and similarity-based responding within participants. Similarity-consistent responses evoked activation in the bilateral anterior cingulate/medial frontal gyrus, the left superior parietal lobe, and the bilateral occipital lobes (Figure 10A). Rule-based responding also activated bilat- eral occipital lobes (see Figure 10B). We did not, how- ever, detect any significant activation elsewhere although exploratory analyses with more liberal thresholds ( p < .001 and 25 contiguous voxels) revealed a similar pattern of activation to what we observed with the similarity re- sponders. We suspect the reduced activation here com- pared with the corresponding analysis for the consistent rule-based sorters reflects the lower number of partici- pants and trials in the current analysis. We found no indi- cation of any differences between strategies in either whole-brain or exploratory ROI analyses. DISCUSSION This study used a negative and positive patterning design originally developed by Shanks and Darby (1998; see also Wills et al., 2011) to compare the brain activation of rule and similarity generalization. Participants were divided into either rule-based or similarity-based generalizers according to their responses during the critical items at test. Importantly, participants in both groups were highly consistent and did not differ in their ability to follow their preferred strategy. There was extensive overlap of activation between the rule- and similarity-based groups, but at the same time there were regions that were dif- ferentially activated by the two strategies. We discuss the most notable aspects of our findings below. In the training phase, there was extensive overlap be- tween groups including diverse regions of the pFC, the parietal lobes, and the occipital lobes. Differences be- tween strategies, in contrast, were more restricted—no regions were more active in the similarity group than the rule group, although the posterior cingulate/precuneus and the anterior cingulate/medial prefontal cortex (in the second half of training) were more active in the rule group than the similarity group. The greater posterior cingulate/ precuneus activation is perhaps somewhat surprising, although these regions have been implicated in rule- based category learning (Milton & Pothos, 2011). The anterior cingulate activation is in line with the key role this region is thought to play in rule selection in COVIS’s rule- based system (Ashby et al., 1998). In the test phase, there was again considerable com- mon overlap in the regions activated by the rule and similarity groups for both the critical generalization trials and the familiar trials. As before, areas activated included regions of the pFC (including the middle frontal gyrus), bilateral parietal lobes, and bilateral occipital lobes. These regions have all previously been implicated in categori- zation tasks (e.g., Carpenter et al., in press; Milton et al., 2009). For example, the bilateral parietal lobes have been heavily implicated in both explicit and implicit classifi- cation of dot patterns (Aizenstein et al., 2000) and stimu- lus generalization (Seger, Braunlich, Wehe, & Liu, 2015). Furthermore, our results add to the growing body of evidence that diverse regions of the pFC are involved in categorization (e.g., Milton & Pothos, 2011; Koenig et al., 2005; Grossman et al., 2002; Seger & Cincotta, 2002). The common activation shared by the rule and similarity groups across both the category learning and generali- zation components of this task is consistent with the idea that both strategies share a number of common, inter- related processes, such as stimulus processing, response selection, stimulus–response mappings, feedback pro- cessing (whether it is an external signal as in training or more internally generated as is likely in test), uncertainty, and attentional and working memory demands to name just a few likely candidates. Although no differences were identified between gen- eralization strategies in whole-brain analyses, exploratory ROI analyses provided evidence for dissociable activa- tion between the rule and similarity groups. As predicted, the rule-based generalizers activated the right middle frontal cortex to a greater extent than the similarity-based generalizers. This was in keeping with the trend from the training phase for there to be greater activation in the right pFC in the rule group than the similarity group. In contrast, the similarity-based generalizers preferentially recruited the anterior medial prefrontal lobe and the right inferior parietal lobes. The greater right middle frontal gyrus activation in rule-based generalizers than similarity-based generalizers is consistent with a broad range of previous work indi- cating that the middle frontal lobes is a critical site for rule- based categorization (e.g., Milton & Pothos, 2011; Seger & Cincotta, 2002, 2005; Tracy et al., 2003; Grossman et al., 2002; Patalano et al., 2001) and, more generally, its role is well established in working memory process- ing (Owen, 2000). This pattern of findings is, therefore, in line with the idea that the “opposites” rule is the result of deliberative, rule-based processes, rather than being driven by a non-rule-based, associatively mediated system (Maes et al., 2015). Turning to the regions preferentially linked to simi- larity responding, the right inferior parietal lobes were identified by Grossman et al. (2002) as being linked to similarity judgments, and they suggested that it had a role in overall feature configuration processing. Alter- natively, it could reflect recollection-based memory pro- cesses, which are often observed in this region (e.g., Milton, Muhlert, Butler, Benattayallah, & Zeman, 2011; Wheeler & Buckner, 2004). This would be consistent with the idea that similarity processing places particular de- mands on the retrieval of past, related instances. We found that the anterior medial prefrontal lobe was activated more for the similarity than the rule group, in a strikingly similar location to that observed by Koenig et al. (2005) in their analogous comparison. Our explanation for this result 162 Journal of Cognitive Neuroscience Volume 29, Number 1 l l / / j t t f / i t . : / / D o w n l o a D d o e w d n l f o r a o d m e d h t f t r p o : m / D / o h m w t i n t t p o p : a r / d c / e . d m s i f i r t o l p m v r e c h r . c p s h i a l d i v i r r e e . r c c t c . o m h m a / i j e r d o . u c c n o o / m c a n / r j a t o r i t c i c c n l e / e - a p - r d p t d i 2 f 9 c / l 1 2 e 9 1 - / 5 p 1 0 d / f 1 1 9 / 5 5 2 0 2 9 / 1 / 1 4 1 9 7 / 1 1 o 4 5 c 2 0 n 5 / _ 2 a 1 / _ 7 j 0 8 o 1 5 c 0 7 n 2 5 4 _ 7 a p / _ d j 0 o 1 b c 0 y n 2 g _ 4 u a . e _ p s 0 t d 1 o f 0 n 2 b 0 4 y 8 . p S M d e I f p T e b m L y i b b e g r r u a 2 e r 0 s i 2 t 3 e s o n u s 0 e 3 r M o a n y / j / f . / t / 1 2 7 0 2 M 1 a y 2 0 2 1 is similar to Koenig et al.’s—the activation in this region may reflect the greater dependence on retrieving specific exemplars from long-term memory in the similarity-based condition rather than having generalization supported by an abstract rule. Of course, there is always a danger in linking specific brain regions to a particular function and in knowing whether the regions identified are essential for the strat- egy used. For example, the fact that both the similarity and rule groups activated different parts of the pFC more than the other strategy appears to challenge any clear-cut narrative that rule generalization requires higher-order, deliberative processes whereas similarity generalization requires more automatic, nondeliberative processes. One intriguing future approach may be to further explore the regions where differences emerged (i.e., the right middle frontal gyrus for the rule generalizers and the anterior medial frontal lobe and right inferior parietal lobes for the similarity generalizers) using TMS. For example, according to our results, one might expect that stimulating the right middle frontal gyrus would disrupt rule generali- zation but leave similarity generalization intact, whereas stimulating the anterior prefrontal lobe (or right inferior parietal lobes) might impair similarity generalization but not rule generalization. Furthermore, if the greater activa- tion in the anterior medial pFC reflects a greater reliance on retrieving past instances in similarity generalization than rule generalization, then one might expect that stimulating this region would disrupt performance in a task using sim- ilar stimuli which more directly assesses memory capacity. Regardless of the precise role that these regions may play, using the Shanks–Darby procedure to examine the neural correlates of rule and similarity generalization makes a valuable new contribution to the area because it over- comes some problems that mar the paradigms used in previous related imaging studies. In particular, many stud- ies in this area confound rule-based and similarity-based learning with single versus multidimensional learning (e.g., Milton & Pothos, 2011; Nomura et al., 2007; Tracy et al., 2003). Furthermore, all previous studies investigat- ing this issue index rule-based learning through behavior that can also be produced by a simple associative mech- anism that incorporates some process of selective atten- tion (e.g., ALCOVE; Kruschke, 1992). In contrast, for the Shanks–Darby procedure, there is no simple associative model that can explain both the similarity- and rule-based generalization observed and the same number of dimen- sions is relevant in both rule- and similarity-based learning. There are other notable differences between the Shanks–Darby procedure and the other tasks previously used (e.g., Nomura et al., 2007; Koenig et al., 2005; Tracy et al., 2003; Grossman et al., 2002), which may have im- pacted the results obtained. For instance, the Shanks– Darby task enables one to partition items into generaliza- tion trials (items not encountered during training) and familiar trials (which had previously been learned during training). In contrast, other studies have either used cat- egory learning (e.g., Carpenter et al., in press; Nomura et al., 2007) or category decision-making (e.g., Milton et al., 2009; Grossman et al., 2002) procedures where there is no generalization phase or used imaging analyses that combine old training items with generalization trials (e.g., Koenig et al., 2005). Given that, in the test phase, we only observed evidence for differences in the gener- alization trials and not in the familiar trials, this could be an important distinction. A second important difference is that previous studies typically use multidimensional stimuli that possess either binary (e.g., Milton et al., 2009; Tracy et al., 2003) or con- tinuous values (e.g., Carpenter et al., in press; Nomura et al., 2007) on a particular dimension. In contrast, our stimuli have discrete components (e.g., A, B, AB, etc.) that are either present or absent. This distinction has not been systematically investigated but could potentially have an important impact on the pattern of results obtained. Clearly, in the future, it would be of value to further ex- plore rule and similarity learning under a more diverse range of conditions to build up a broader understanding of how the two types of strategies relate to each other. Although the Shanks–Darby procedure appears to have a number of strengths, one potential limitation is that, although the rule and similarity groups were well matched in their consistency of applying their preferred strategy for the critical generalization trials, the rule group signif- icantly outperformed the similarity group for the familiar training trials (this is the same pattern that Shanks & Darby, 1998, found). It is worth noting, though, that the familiar trials were analyzed separately from the critical generalization trials where the neural differences emerged, which should attenuate any influence this dif- ference would have on our results. Furthermore, differ- ences between groups for the familiar items (where only correct responses were considered) were extremely re- stricted, which suggests that performance difference between groups had little impact on the pattern of activa- tion. Nevertheless, in future work with the Shanks–Darby procedure, it would be desirable to have the groups better matched on performance for the familiar trials. One way of doing this could be to introduce a learning criterion during the training phase, which has been shown to better equate groups on the familiar test items in this procedure (Wills et al., 2011). Another notable aspect of our results is that the key comparison between rule and similarity generalization was between participants. While we looked at this from a within-subject perspective as well by considering those participants who displayed a mixture of strategies, these analyses were not particularly revealing. We suspect that this was due to these participants randomly responding on the critical trials and/or that there were insufficient trials of each type to reliably detect any differences. In the future, one could potentially further increase the number of critical generalization trials in the procedure and/or use a combination of more effective instructions Milton et al. 163 l l / / j f / t t i t . : / / D o w n l o a D d o e w d n l f o r a o d m e d h t f t r p o : m / D / o h m w t i n t t p o p : a r / d c / e . d m s i f i r t o l p m v r e c h r . c p s h i a l d i v i r r e e . r c c t c . o m h m a / i j e r d o . u c c n o o / m c a n / r j a t o r i t c i c c n l e / e - a p - r d p t d i 2 f 9 c / l 1 2 e 9 1 - / 5 p 1 0 d / f 1 1 9 / 5 5 2 0 2 9 / 1 / 1 4 1 9 7 / 1 1 o 4 5 c 2 0 n 5 / _ 2 a 1 / _ 7 j 0 8 o 1 5 c 0 7 n 2 5 4 _ 7 a p / _ d j 0 o 1 b c 0 y n 2 g _ 4 u a . e _ p s 0 t d 1 o f 0 n 2 b 0 4 y 8 . p S M d e I f p T e b m L y i b b e g r r u a 2 e r 0 s i 2 t 3 e s o n u s 0 e 3 r M o a n y / j / . t / f / 1 2 7 0 2 M 1 a y 2 0 2 1 than we used with training outside the scanner to ensure that participants produce a good mixture of rule and similarity generalization responses. Another limitation of this study is that, although the differences between generalization strategies were con- sistent with our a priori predictions and in line with past related work, it must be acknowledged that these differ- ences were not identifiable in whole-brain analyses and could only be detected in more exploratory, post hoc analyses. Clearly, it would be valuable for future work to try and replicate our basic pattern of results. Nevertheless, our finding of differences in activation between rule and similarity generalization (albeit only in exploratory analyses) is in line with recent behavioral and comparative studies that have been conducted with the Shanks–Darby procedure. Specifically, Wills et al. (2011) found that participants who completed the train- ing session under a concurrent load went on to produce significantly less rule-based generalization than participants who undertook training under no load. Wills et al. sug- gested that this is consistent with the idea that discovering the “opposites” rule requires considerable working mem- ory capacity and if this is not available that participants will fail to transition from using a similarity approach to a rule-based approach. Related to this, Maes et al. (2015) found that, although humans (under no concurrent load) could readily discover the opposites rule, both pigeons and rats were unable to do so and relied on similarity generalization. This is consistent with the idea that pigeons and rats may be forced to rely on the similarity system whereas humans also have access to a rule-based system. Our findings complement these two recent behavioral studies by identifying specific neural correlates that are associated with rule and similarity-based generalization. However, although our results make a novel and valu- able contribution to the area, our findings stop some way short of providing clear evidence for qualitatively sepa- rable generalization systems by any reasonable definition. For instance, neural differences in themselves should not be taken as evidence for separable systems given that past work has shown that items within the same category can provoke differential activation (e.g., Davis & Poldrack, 2014; DeGutis & D’Esposito, 2009; Grinband, Hirsch, & Ferrera, 2006). What may be more compelling evidence for separable learning systems would be large differences in activation in regions that are not also activated by the other strategy. This does not appear to be the case in this study, where the differences in generalization were re- stricted and located close to regions that were activated in training and test by both strategies. Furthermore, the commonalities in activation between strategies clearly out- weigh the differences. One potentially fruitful way of viewing the current data, then, is to consider category learning and stimulus gen- eralization as cognitively complex processes comprising a number of subcomponents (e.g., stimulus processing, hypothesis testing, decision-making, feedback process- ing, etc.) many of which are likely to be shared by rule and similarity strategies. Furthermore, one strategy may place more of an emphasis on one of these subprocesses than the other strategy does. For example, the “oppo- sites” rule needed for rule generalization appears likely to place particular demands on working memory capacity and rule formation that do not appear to be needed to the same extent for similarity generalization and this may re- quire increased activation of the right middle frontal gyrus. Conversely, a similarity strategy may, for example, impose higher memory demands than the rule condition (where there may be more of an emphasis on abstract rules), which could lead to greater engagement of the anterior medial prefrontal lobes. Of course, our hypotheses as to what particular role these brain regions play may not be correct but as others have recommended (e.g., Davis, Love, & Preston, 2012) trying to link brain regions to spe- cific functions of the learning process may be at least as profitable an approach as focusing on the more general and contentious question of whether data is more in line with single-or multiple-system accounts. We suggest that further examination of the Shanks–Darby procedure, with its notable strengths, could play an important role in fur- ther illuminating both these important questions. Acknowledgments We thank the Experimental Psychology Society for their support and two anonymous reviewers for their insightful and construc- tive comments. Reprint requests should be sent to Fraser Milton, Discipline of Psychology, University of Exeter, Exeter, EX4 4QG, United Kingdom, or via e-mail: f.n.milton@ex.ac.uk. Notes 1. The raw imaging and behavioral data are available for inter- ested readers at www.willslab.co.uk/exe10/index.html. 2. By convention, a BF of over three is interpreted as pro- viding substantial evidence for the experimental hypothesis ( Jeffreys, 1961), whereas a BF below a third provides substan- tial evidence for the null (Dienes, 2011). BF analysis requires an estimate of the mean expected difference under the experi- mental hypothesis; we estimated this from the observed differ- ence for the familiar test items. Following Dienes (2011), the expected difference was modeled as a two-tailed normal distri- bution with a standard deviation equal to half the mean. Calcu- lations were run using a custom script (Baguley & Kaye, 2010) within R (R Core Team, 2015). REFERENCES Aizenstein, H. S., MacDonald, A. 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Quarterly Journal of Experimental Psychology, 66, 299–318. l l / / j t t f / i t . : / / D o w n l o a D d o e w d n l f o r a o d m e d h t f t r p o : m / D / o h m w t i n t t p o p : a r / d c / e . d m s i f i r t o l p m v r e c h r . c p s h i a l d i v i r r e e . r c c t c . o m h m a / i j e r d o . u c c n o o / m c a n / r j a t o r i t c i c c n l e / e - a p - r d p t d i 2 f 9 c / l 1 2 e 9 1 - / 5 p 1 0 d / f 1 1 9 / 5 5 2 0 2 9 / 1 / 1 4 1 9 7 / 1 1 o 4 5 c 2 0 n 5 / _ 2 a 1 / _ 7 j 0 8 o 1 5 c 0 7 n 2 5 4 _ 7 a p / _ d j 0 o 1 b c 0 y n 2 g _ 4 u a . e _ p s 0 t d 1 o f 0 n 2 b 0 4 y 8 . p S M d e I f p T e b m L y i b b e g r r u a 2 e r 0 s i 2 t 3 e s o n u s 0 e 3 r M o a n y / j / / / t f . 1 2 7 0 2 M 1 a y 2 0 2 1 166 Journal of Cognitive Neuroscience Volume 29, Number 1 The Neural Correlates of Similarity- and image

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